import numpy as np
import platgo as pg
import scipy.io as sio


class CEC_2020_F4(pg.Problem):

    def __init__(self, D=None) -> None:
        self.name = 'CEC_2020_F4'
        self.type['single'], self.type['real'] = [True] * 2
        self.M = 1
        load_path = 'CEC2020.mat'
        load_data = sio.loadmat(load_path)
        mat = []
        for k in load_data.items():
            mat.append(k)
        self.D = D
        self.O = mat[3][1][0][3][0][0][0]
        if self.D is None or self.D < 10:
            self.D = 5
            self.Mat = mat[3][1][0][3][0][0][1]
        elif self.D < 15:
            self.D = 10
            self.Mat = mat[3][1][0][3][0][0][2]
        elif self.D < 20:
            self.D = 15
            self.Mat = mat[3][1][0][3][0][0][3]
        else:
            self.D = 20
            self.Mat = mat[3][1][0][3][0][0][4]
        lb = [-100] * self.D
        ub = [100] * self.D
        self.borders = np.array([lb, ub])
        super().__init__()

    def cal_obj(self, pop: pg.Population) -> None:
        Z = pop.decs - np.tile(self.O[0][0: pop.decs.shape[1]], (pop.decs.shape[0], 1))
        Y = 0.05 * np.dot(Z, self.Mat.T)
        Z = Y + 1
        temp = 100 * (Z ** 2 - np.hstack((Z[:, 1:], Z[:, 0:1]))) ** 2 + (Z - 1) ** 2
        pop.objv = 1900 + np.sum(temp ** 2 / 4000 - np.cos(temp) + 1, axis=1)
        pop.objv = pop.objv.reshape(pop.objv.shape[0], 1)

    def get_optimal(self) -> np.ndarray:
        pass


if __name__ == '__main__':
    problem = CEC_2020_F4()
    alg = pg.algorithms.GA(problem=problem, maxgen=100)
    pop = alg.go(100)
    print(pop)
